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PurposeTo compare the utility of single versus repeated autism screening in a sample at higher likelihood (HL) for ASD, following both screen positives and all screen negatives to diagnostic outcome.MethodsUsing a prospective infant sibling design, the current study followed 135 toddlers at HL for ASD and conducted diagnostic evaluations on the full sample at 18, 24, and 36 months. The psychometric properties of the M-CHAT-R using both concurrent and predictive diagnostic evaluations were compared in a group screened once (at 18 months only, n = 60) or twice (at both 18 and 24 months, n = 75). The study also examined consistency in reporting of ASD symptoms across the M-CHAT-R and a developmental concerns interview, comparing the HL group to a group with lower likelihood (LL) for ASD (n = 88).ResultsSensitivity and specificity of the M-CHAT-R were high (75 - 95%), consistent with previous research. Positive predictive value (43 - 76%) was higher in this HL group than in previous community samples. Repeat screening improved sensitivity with little cost to specificity. At both 18 and 24 months, HL parents were more consistent in their reporting on the M-CHAT-R and a concerns interview than LL parents.ConclusionThe M-CHAT-R has strong psychometric properties when used with groups at HL for ASD, suggesting that scores over the screening cutoff of 3 should lead to prompt diagnostic evaluation referrals in children with older siblings on the spectrum.

A screening questionnaire for autism symptoms is not yet available in Poland, and there are no recommendations regarding screening for developmental disorders in Polish primary healthcare. The aim of this study was to assess the opinions of parents and physicians on the legitimacy and necessity of screening for autism spectrum disorders, potential barriers to the implementation of the screening program, and the evaluation and presentation of the process of online ASD screening, which was part of the validation program for the Polish version of one of the screening tools. This study involved 418 parents whose children were screened online and 95 primary care physicians who expressed their opinions in prepared surveys. The results indicate that both parents and doctors perceive the need to screen children for ASD in the general population without a clear preference as to the screening method (online or in person). Moreover, online screening is considered by respondents as a satisfactory diagnostic method. Therefore, online screening may prove to be at least a partial method of solving numerous obstacles indicated by participants' systemic difficulties including time constraints, the lack of experienced specialists in the field of developmental disorders and organizational difficulties of healthcare systems. [ABSTRACT FROM AUTHOR]

Autism is a relatively common neurodevelopmental condition that affects social communication and behavior, affecting the overall quality of life. The prevalence of autism is constantly increasing, but timely diagnosis allows for effective treatment. The aim of our research was to construct a Russian version of the RAADS-14, a brief self-report questionnaire originally designed for autism screening, and to perform its initial validation to provide a reference point in developing autism self-assessment tools for the Russian-speaking population. Psychometric properties of the RAADS-14 Rus were evaluated on a sample of 1724 participants, including a general sample of non-psychiatric young adults (n = 794) and adults with clinically established ASD (n = 49); a brief Russian inventory assessing Big Five personality traits (FFQ) was administered to a part of the sample (n = 364) to provide the first assessment of criterion validity. Confirmatory factor analysis of the RAADS-14 Rus confirmed the factor structure of the original Swedish version yielding acceptable fit indices. The discriminating properties were substantially worse than in the original study. The correlations between the RAADS-14 Rus domain scores and the Big Five factors were similar to previously obtained findings. Overall, the results suggest that the RAADS-14 Rus can be used as a screening tool for ASD in adults with proper caution and considering its discriminating properties.

ASD (autism spectrum disorder) is a complex developmental and neurological disorder that impacts the social life of the affected person by disturbing their capability for interaction and communication. As it is a behavioural disorder, early treatment will improve the quality of life of ASD patients. Traditional screening is carried out with behavioural assessment through trained physicians, which is expensive and time-consuming. To resolve the issue, several conventional methods strive to achieve an effective ASD identification system, but are limited by handling large data sets, accuracy, and speed. Therefore, the proposed identification system employed the MBA (modified bat) algorithm based on ANN (artificial neural networks), modified ANN (modified artificial neural networks), DT (decision tree), and KNN (k-nearest neighbours) for the classification of ASD in children and adolescents. A BA (bat algorithm) is utilised for the automatic zooming capability, which improves the system's efficacy by excellently finding the solutions in the identification system. Conversely, BA is effective in the identification, it still has certain drawbacks like speed, accuracy, and falls into local extremum. Therefore, the proposed identification system modifies the BA optimisation with random perturbation of trends and optimal orientation. The dataset utilised in the respective model is the Q-chat-10 dataset. This dataset contains data of four stages of age groups such as toddlers, children, adolescents, and adults. To analyse the quality of the dataset, dataset evaluation mechanism, such as the Chi-Squared Statistic and p-value, are used in the respective research. The evaluation signifies the relation of the dataset with respect to the proposed model. Further, the performance of the proposed detection system is examined with certain performance metrics to calculate its efficiency. The outcome revealed that the modified ANN classifier model attained an accuracy of 1.00, ensuring improved performance when compared with other state-of-the-art methods. Thus, the proposed model was intended to assist physicians and researchers in enhancing the diagnosis of ASD to improve the standard of life of ASD patients. [ABSTRACT FROM AUTHOR]

Autism is a relatively common neurodevelopmental condition that affects social communication and behavior, affecting the overall quality of life. The prevalence of autism is constantly increasing, but timely diagnosis allows for effective treatment. The aim of our research was to construct a Russian version of the RAADS-14, a brief self-report questionnaire originally designed for autism screening, and to perform its initial validation to provide a reference point in developing autism self-assessment tools for the Russian-speaking population. Psychometric properties of the RAADS-14 Rus were evaluated on a sample of 1724 participants, including a general sample of non-psychiatric young adults (n = 794) and adults with clinically established ASD (n = 49); a brief Russian inventory assessing Big Five personality traits (FFQ) was administered to a part of the sample (n = 364) to provide the first assessment of criterion validity. Confirmatory factor analysis of the RAADS-14 Rus confirmed the factor structure of the original Swedish version yielding acceptable fit indices. The discriminating properties were substantially worse than in the original study. The correlations between the RAADS-14 Rus domain scores and the Big Five factors were similar to previously obtained findings. Overall, the results suggest that the RAADS-14 Rus can be used as a screening tool for ASD in adults with proper caution and considering its discriminating properties. [ABSTRACT FROM AUTHOR]

Autism is a neurological and developmental disability caused by changes in the brain's development that also affects the facial tissues. Thus, children with autism show distinct facial features that are not present in average children. Studies reveal increasing prevalence of autism; however, acquiring affordable, trouble-free, and practical early screening tools is a current concern. This impacted early detection and diagnosis of autism which also influenced effective intervention. How can we employ innovative technology, like computer vision and deep learning to build an inexpensive and universally accessible autism screener to prevent late detection and diagnosis of autism? How can we enhance public access to this screening tool and minimize the difficulties involved in the assessment process? We built a basic Convolutional Neural Network (CNN) binary image classifier with seven (7) layers including the input and output layers. This initial model produced positive outcomes with a specificity score of 90.38% and this is the most important evaluation metric for health-related problems like screening for autism. We optimized this model by performing hyperparameter tuning using a cloud machine learning platform, Amazon SageMaker. The tuning job also produced a superior and robust model as reflected in the F1 score of 94.74%. It correctly classified 95% of the images. The model's specificity indicates it correctly identified 100% of those without autism as non-autistic; the recall indicates it correctly identified 90% of those with autism as autistic while its precision indicates a 100% probability that those identified by the model as autistic have autism. Tuning this model took 6 minutes. We integrated this model into a simple iOS application for mobile devices. [ABSTRACT FROM AUTHOR]

Prior studies suggest autism-specific and general developmental screens are complementary for identifying both autism and developmental delay (DD). Parents completed autism and developmental screens before 18-month visits. Children with failed screens for autism (n = 167) and age, gender, and practice-matched children passing screens (n = 241) completed diagnostic evaluations for autism and developmental delay. When referral for autism and/or DD was considered, overall false positives from the autism screens were less frequent than for referral for autism alone. Presence of a failed communication subscale in the developmental screen was a red flag for autism and/or DD. An ordinally-scored autism screen had more favorable characteristics when considering autism and/or DD, yet none of the screens achieved recommended standards at 18 months, reinforcing the need for recurrent screening as autism emerges in early development. [ABSTRACT FROM AUTHOR]

A screening questionnaire for autism symptoms is not yet available in Poland, and there are no recommendations regarding screening for developmental disorders in Polish primary healthcare. The aim of this study was to assess the opinions of parents and physicians on the legitimacy and necessity of screening for autism spectrum disorders, potential barriers to the implementation of the screening program, and the evaluation and presentation of the process of online ASD screening, which was part of the validation program for the Polish version of one of the screening tools. This study involved 418 parents whose children were screened online and 95 primary care physicians who expressed their opinions in prepared surveys. The results indicate that both parents and doctors perceive the need to screen children for ASD in the general population without a clear preference as to the screening method (online or in person). Moreover, online screening is considered by respondents as a satisfactory diagnostic method. Therefore, online screening may prove to be at least a partial method of solving numerous obstacles indicated by participants’ systemic difficulties including time constraints, the lack of experienced specialists in the field of developmental disorders and organizational difficulties of healthcare systems.

ASD (autism spectrum disorder) is a complex developmental and neurological disorder that impacts the social life of the affected person by disturbing their capability for interaction and communication. As it is a behavioural disorder, early treatment will improve the quality of life of ASD patients. Traditional screening is carried out with behavioural assessment through trained physicians, which is expensive and time-consuming. To resolve the issue, several conventional methods strive to achieve an effective ASD identification system, but are limited by handling large data sets, accuracy, and speed. Therefore, the proposed identification system employed the MBA (modified bat) algorithm based on ANN (artificial neural networks), modified ANN (modified artificial neural networks), DT (decision tree), and KNN (k-nearest neighbours) for the classification of ASD in children and adolescents. A BA (bat algorithm) is utilised for the automatic zooming capability, which improves the system’s efficacy by excellently finding the solutions in the identification system. Conversely, BA is effective in the identification, it still has certain drawbacks like speed, accuracy, and falls into local extremum. Therefore, the proposed identification system modifies the BA optimisation with random perturbation of trends and optimal orientation. The dataset utilised in the respective model is the Q-chat-10 dataset. This dataset contains data of four stages of age groups such as toddlers, children, adolescents, and adults. To analyse the quality of the dataset, dataset evaluation mechanism, such as the Chi-Squared Statistic and p-value, are used in the respective research. The evaluation signifies the relation of the dataset with respect to the proposed model. Further, the performance of the proposed detection system is examined with certain performance metrics to calculate its efficiency. The outcome revealed that the modified ANN classifier model attained an accuracy of 1.00, ensuring improved performance when compared with other state-of-the-art methods. Thus, the proposed model was intended to assist physicians and researchers in enhancing the diagnosis of ASD to improve the standard of life of ASD patients.

Abstract Background Autism screening is recommended at 18- and 24-month pediatric well visits. The Modified Checklist for Autism in Toddlers—Revised (M-CHAT-R) authors recommend a follow-up interview (M-CHAT-R/F) when positive. M-CHAT-R/F may be less accurate for 18-month-olds than 24-month-olds and accuracy for identification prior to two years is not known in samples that include children screening negative. Since autism symptoms may emerge gradually, ordinally scoring items based on the full range of response options, such as in the 10-item version of the Quantitative Checklist for Autism in Toddlers (Q-CHAT-10), might better capture autism signs than the dichotomous (i.e., yes/no) items in M-CHAT-R or the pass/fail scoring of Q-CHAT-10 items. The aims of this study were to determine and compare the accuracy of the M-CHAT-R/F and the Q-CHAT-10 and to describe the accuracy of the ordinally scored Q-CHAT-10 (Q-CHAT-10-O) for predicting autism in a sample of children who were screened at 18 months. Methods This is a community pediatrics validation study with screen positive (n = 167) and age- and practice-matched screen negative children (n = 241) recruited for diagnostic evaluations completed prior to 2 years old. Clinical diagnosis of autism was based on results of in-person diagnostic autism evaluations by research reliable testers blind to screening results and using the Autism Diagnostic Observation Schedule—Second Edition (ADOS-2) Toddler Module and Mullen Scales of Early Learning (MSEL) per standard guidelines. Results While the M-CHAT-R/F had higher specificity and PPV compared to M-CHAT-R, Q-CHAT-10-O showed higher sensitivity than M-CHAT-R/F and Q-CHAT-10. Limitations Many parents declined participation and the sample is over-represented by higher educated parents. Results cannot be extended to older ages. Conclusions Limitations of the currently recommended two-stage M-CHAT-R/F at the 18-month visit include low sensitivity with minimal balancing benefit of improved PPV from the follow-up interview. Ordinal, rather than dichotomous, scoring of autism screening items appears to be beneficial at this age. The Q-CHAT-10-O with ordinal scoring shows advantages to M-CHAT-R/F with half the number of items, no requirement for a follow-up interview, and improved sensitivity. Yet, Q-CHAT-10-O sensitivity is less than M-CHAT-R (without follow-up) and specificity is less than the two-stage procedure. Such limitations are consistent with recognition that screening needs to recur beyond this age.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with rising prevalence, necessitating early diagnosis and intervention. This case report examines the clinical diagnosis approach of ASD in children under two years, emphasizing motor developmental delay, chromosome 19 mutations, prematurity, macrocephaly, and false-negative Modified Checklist for Autism in Toddlers (MCHAT) results. This study identifies gross motor delays as a potential key indicator in the diagnosis of ASD, as all five cases (Patients A, B, C, D, and E) were observed to have such deficits. Two cases (Patients A and B) initially had negative MCHAT results but were later diagnosed with ASD. Patients C and E both had a chromosome 19 abnormality. Patient E had macrocephaly and an amino acid metabolism disorder. ASD atypical behaviors like hand flapping, wringing, and twirling were also noted. Our systematic review validated the key findings presented in this study, unveiling a consistent pattern throughout the existing literature about ASD diagnoses and the associated misconceptions. These cases highlight the significance of early motor delay, genetic factors, and the limitations of MCHAT in early ASD diagnosis. This case report underscores the need for improved screening tools, genetic investigations, and comprehensive assessments to enhance early detection and intervention for ASD. Early identification and personalized interventions hold a promise to improve the outcomes and quality of life for children with autism., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Fernandez et al.)

The high co-occurrence of autism and eating disorders is well established, including for those with Avoidant Restrictive Food Intake Disorder (ARFID). It is therefore important to consider autism and identify possible autism when people present to eating disorder services to ascertain whether further assessment is indicated, to support clinical formulation and to make appropriate adaptations during interventions. This paper explores the utility of a validated autism screening measure, the AQ-10, in a population of children and adolescents who presented to an outpatient eating disorders clinic for an assessment of possible ARFID. Over 19 months, 335 young people were assessed and 246 families with children aged between 4 and 17 years completed one of three versions of the AQ-10 (Child, Adolescent, and Adult), as part of a battery of routinely administered pre-assessment questionnaires. Results indicated that 80.2% (n = 69) of those with an existing autism diagnosis scored above clinical threshold of ≥6 (M = 7.2, SD = 1.9), 43.9% (n = 43) of those queried to be autistic scored above clinical threshold (M = 5.2, SD = 2.5), and 6.5% (n = 4) of non-autistic individuals scored above clinical threshold (M = 2.8, SD = 1.8). Additionally, the AQ-10 satisfactorily discriminated between those with a known autism diagnosis and those who are not autistic across all age groups and sex. We conclude that the AQ-10, alongside a comprehensive clinical assessment and clinical judgment, is a useful screening tool that can support clinicians to identify appropriate onward referrals for autism assessments, aid clinical formulation, and consider appropriate adaptations and reasonable adjustments during ARFID interventions., (© 2024 International Society for Autism Research and Wiley Periodicals LLC.)

Evolutionary computing algorithms are computational intelligent systems that are used in a wide range of research applications, primarily for optimization. In this paper, an artificial neural network (ANN), a machine learning technique, is used to classify the data. The weights associated with each neuron and the architecture of the neural network always bias the output of the network model. With prior knowledge or trial and error techniques, different metrics or objectives can be used to optimise these weights. The optimization of weights using multiple objectives refers to a "multi-objective optimization problem." In this paper, an evolutionary cultural algorithm is used to optimise weights in ANN, and the results are reported with improved accuracy. Three benchmark datasets for autism screening data are used, trained, and tested for model accuracy in the classification: toddlers (1054,19), children (292,21), and adults (704,21).With the support of the domain expert, real-time data were collected from parents and caregivers and totalled over 1000 records, with a moderate difference in attributes based on CARS-2 (Childhood Autism Rating Scale, 2nd Edition) for ASD screening. In this paper, the proposed model is compared using a curve-fitting mathematical technique. The proposed model is trained and tested, and the results showed that it outperformed other algorithms in terms of precision, accuracy, sensitivity, and specificity. [ABSTRACT FROM AUTHOR]

Evaluations of early screening tests for autism commonly rely on receiver operating characteristic (ROC) analysis and comparisons of area under the curve (AUC). Whether AUC differs significantly from chance or between test items is not always assessed. Two recent and independent evaluations of the Brief Autism Detection in Early Childhood (BADEC) constructed a short-form by selecting the five items with the highest AUC values, leading to inconsistencies regarding appropriate item content (Nah et al., 2018; Nevill et al., 2019). Using significance testing to compare AUC values for each test item from each dataset, we demonstrate which items justify inclusion in the BADEC, which items can be ruled out, and highlight key factors influencing AUC significance testing outcomes. [ABSTRACT FROM AUTHOR]

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Universal autism screening is recommended by the American Academy of Pediatrics at 18 and 24 months. However, many children are not identified until after the age of 4 years, and some not until adulthood, either due to mild or no indication of symptoms early in development, or to co-occurring conditions which may overshadow autism symptoms. This indicates a need for universal autism screening measures for school-age children. This project adapts the widely used toddler autism screening tool, the Modified Checklist for Autism in Toddlers, Revised, with Follow-Up (M-CHAT-R/F), for use in school-age children, called M-CHAT-School (M-CHAT-S). The study follows the Patient-Reported Outcomes Measurement Information System guidelines for measure development to create parent- and teacher-report versions of the M-CHAT-S for 4- to 8-year-old children. Through expert consensus feedback via a Delphi pool and cognitive interviewing with stakeholders (i.e. parents and teachers), we developed two versions of the M-CHAT-S to be used for verbal and minimally verbal children. The M-CHAT-S poses several advantages to existing measures, including brevity, items updated based on current knowledge and conventions, and narrow age range to assure items are developmentally appropriate. Future steps include validation of the M-CHAT-S to determine its utility as an autism screener for young school-age children.The American Academy of Pediatrics recommends universal screening to identify children at higher likelihood for autism at 18- and 24-month well-child visits. There are many children, however, that are missed during this toddler age who do not get diagnosed until much later in development, delaying access to autism-specific interventions. Currently, brief measures for universal autism screening for school-age children, however, are lacking. In this project, we adapted a commonly used autism screener for toddlers, the Modified Checklist for Autism in Toddlers, Revised, with Follow-Up (M-CHAT-R/F), to be used for school-age children. This measure, called the M-CHAT-School (M-CHAT-S), is a parent- and teacher-report questionnaire to be used to screen for autism in school-age children aged 4 to 8 years of age. M-CHAT-S was developed through feedback from autism experts, as well as interviews with parents and teachers to provide input on the items. Two versions of M-CHAT-S were developed, one for verbally fluent and one for minimally verbal school-age children. M-CHAT-S is a brief measure, with updated items to reflect changes in the way experts think and talk about autism, making it a useful measure to use for autism screening in elementary aged children. The next steps include further testing to ensure that M-CHAT-S performs well in identifying children with increased likelihood of autism, after which it will be made available to parents, educators, and other professionals. [ABSTRACT FROM AUTHOR]

Background: Autism screening is recommended at 18- and 24-month pediatric well visits. The Modified Checklist for Autism in Toddlers—Revised (M-CHAT-R) authors recommend a follow-up interview (M-CHAT-R/F) when positive. M-CHAT-R/F may be less accurate for 18-month-olds than 24-month-olds and accuracy for identification prior to two years is not known in samples that include children screening negative. Since autism symptoms may emerge gradually, ordinally scoring items based on the full range of response options, such as in the 10-item version of the Quantitative Checklist for Autism in Toddlers (Q-CHAT-10), might better capture autism signs than the dichotomous (i.e., yes/no) items in M-CHAT-R or the pass/fail scoring of Q-CHAT-10 items. The aims of this study were to determine and compare the accuracy of the M-CHAT-R/F and the Q-CHAT-10 and to describe the accuracy of the ordinally scored Q-CHAT-10 (Q-CHAT-10-O) for predicting autism in a sample of children who were screened at 18 months. Methods: This is a community pediatrics validation study with screen positive (n = 167) and age- and practice-matched screen negative children (n = 241) recruited for diagnostic evaluations completed prior to 2 years old. Clinical diagnosis of autism was based on results of in-person diagnostic autism evaluations by research reliable testers blind to screening results and using the Autism Diagnostic Observation Schedule—Second Edition (ADOS-2) Toddler Module and Mullen Scales of Early Learning (MSEL) per standard guidelines. Results: While the M-CHAT-R/F had higher specificity and PPV compared to M-CHAT-R, Q-CHAT-10-O showed higher sensitivity than M-CHAT-R/F and Q-CHAT-10. Limitations: Many parents declined participation and the sample is over-represented by higher educated parents. Results cannot be extended to older ages. Conclusions: Limitations of the currently recommended two-stage M-CHAT-R/F at the 18-month visit include low sensitivity with minimal balancing benefit of improved PPV from the follow-up interview. Ordinal, rather than dichotomous, scoring of autism screening items appears to be beneficial at this age. The Q-CHAT-10-O with ordinal scoring shows advantages to M-CHAT-R/F with half the number of items, no requirement for a follow-up interview, and improved sensitivity. Yet, Q-CHAT-10-O sensitivity is less than M-CHAT-R (without follow-up) and specificity is less than the two-stage procedure. Such limitations are consistent with recognition that screening needs to recur beyond this age. [ABSTRACT FROM AUTHOR]

It is the current consensus amongst the psychiatric community that children undergoing evaluation for developmental delays and/or autism spectrum disorder (ASD) should be offered genetic testing early in the diagnostic process. Identifying genetic abnormalities can provide insight into patient prognosis and may reveal other medical complications that could arise throughout a patient's life. Despite these recognized benefits, genetic testing is often delayed or not offered and therefore deprives families of valuable knowledge about their child's future health outcomes. We present a case of a six-year-old patient who presented to our child and adolescent psychiatry office for behavioral concerns. She had received an ASD diagnosis years prior to presentation, but for unknown reasons, genetic testing had never been pursued. Genetic testing was obtained in our office, and the results revealed three different mutations that were linked to ASD and various other medical complications including epilepsy. With this knowledge, the patient's family gained important insight into their child's prognosis. This case highlights the necessity for adopting a point-of-care testing (POCT) model when evaluating children with developmental delays and/or ASD. Through this model, genetic testing would be offered to families during the initial visit for these patients. This would help streamline this process and allow for more widespread detection of genetic disorders linked to ASD and coexisting medical sequelae. Having this knowledge would empower families with a better understanding of their child's condition and would allow families to work together with providers to determine the best possible treatment plan., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Kessler et al.)

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a rising prevalence worldwide. While genetic factors are significantly associated with the disorder, environmental factors are often speculated to contribute to its onset. The Middle East, exhibiting higher rates of ASD, also sees frequent consanguineous marriages, necessitating focused studies on potential etiological factors in the region. We report a unique case of a family with three children diagnosed with ASD. The parents, aged between 35 and 39 years at the birth of their first child, have no notable familial history of neurodevelopmental disorders. Interestingly, while both parents and two of the children had normal chromosomal patterns, one child displayed chromosomal abnormalities. This discrepancy raises questions about the interplay between genetics and external factors in the manifestation of ASD. The family's medical history, combined with the regional context of high ASD prevalence and consanguineous marriages, provides a compelling backdrop for the study. The presence of chromosomal abnormalities in only one child, despite no detectable genetic irregularities in parents or siblings, underscores the potential influence of environmental factors in the development of ASD. This case accentuates the importance of conducting in-depth genetic and environmental studies to unravel the intricate etiological web surrounding ASD in the Middle East., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Kamal et al.)

Objectives: Autism Spectrum Disorder (ASD) is a complex range of neurodegenerative conditions that impact individuals' social behaviour and communication skills. However, ASD data often contains far more controls than cases. This poses a serious challenge when creating classification models due to deriving models that favour controls during the classification of individuals. This problem is known as class imbalance, and it may reduce the performance in classification models derived by machine learning (ML) techniques due to individuals may remain undetected.Methods: ML appears to help in the distressing disorder by improving outcome quality besides speeding up the access to early diagnosis and consequential treatment. A screening dataset that consists of over 1100 instances was used to perform extensive quantitative analysis using different data resampling techniques and according to specific evaluation metrics. We measure the effect of class imbalance on autism screening performance using different data resampling techniques with a ML classifier and with respect to sensitivity, specificity, and F1-measure. We would like to know which resampling methods work well in balancing autism screening data.Results: The results reveal that data resampling, and especially oversampling, improve results derived by the considered ML classifier. More importantly, there was superiority in terms of sensitivity and specificity for models derived by Naive Bayes classifier when oversampling methods have been used for data pre-processing on the autism data considered.Conclusion: The results reported encourages further improvement of the design and implementation of ASD screening systems using intelligent technology. [ABSTRACT FROM AUTHOR]

Early screening and diagnosis of autism spectrum disorder (ASD) primarily rely on behavioral observations by qualified clinicians whose decision process can benefit from the combination of machine learning algorithms and sensor data. We designed a computerized visual‐orienting task with gaze‐related or non‐gaze‐related directional cues, which triggered participants' gaze‐following behavior. Based on their eye‐movement data registered by an eye tracker, we applied the machine learning algorithms to classify high‐functioning children with ASD (HFA), low‐functioning children with ASD (LFA), and typically developing children (TD). We found that TD children had higher success rates in obtaining rewards than HFA children, and HFA children had higher rates than LFA children. Based on raw eye‐tracking data, our machine learning algorithm could classify the three groups with an accuracy of 81.1% and relatively high sensitivity and specificity. Classification became worse if only data from the gaze or nongaze conditions were used, suggesting that "less‐social" directional cues also carry useful information for distinguishing these groups. Our findings not only provide insights about visual‐orienting deficits among children with ASD but also demonstrate the promise of combining classical behavioral paradigms with machine learning algorithms for aiding the screening for individuals with ASD. [ABSTRACT FROM AUTHOR]

Background: There is a shortage of validated autism screening tests in the 11 official languages of South Africa. The Modified Checklist for Autism in Toddlers, Revised with Follow-Up (M-CHAT-R/FTM), a validated and well-known screening test, had already been adapted (in English) and translated into Northern Sotho for use in South Africa. Objectives: The aim was to collect pilot data to determine the preliminary reliability and feasibility of the two tests to confirm the equivalence of the adaptation and translation. Method: The study was conducted in a peri-urban community in South Africa. Twenty-one first-language Northern Sotho caregivers of children aged between 18 and 48 months were recruited by employing snowball sampling. The participants were asked to complete the Northern Sotho and the culturally adapted English M-CHAT-R/F, which were presented in random order. Results: The preliminary content validity and equivalence were evident, with no difference at the 5% interval of the Wilcoxon signed rank test. All 21 toddlers screened presented with a low risk for autism following the recommended execution of the Follow-Up section for the toddlers in the medium risk category. All participants completed the two screening tests, with none indicating unfamiliar words or constructs. A higher preference for the English adapted version was found but a need for the Northern Sotho screening test was also evident Conclusion: The Northern Sotho translation of the M-CHAT-R/F, as well as the adapted English version, appears feasible and is ready for comprehensive validation.

Early identification of autism, followed by appropriate intervention, has the potential to improve outcomes for autistic individuals. Numerous screening instruments have been developed for children under 3 years of age. Level 1 screeners are used in large-scale screening to detect at-risk children in the general population; Level 2 screeners are concerned with distinguishing children with signs of autism from those with other developmental problems. The focus here is evaluation of Level 2 screeners. However, given the contributions of Level 1 screeners and the necessity to understand how they might interface with Level 2 screeners, we briefly review Level 1 screeners and consider instrument characteristics and system variables that may constrain their effectiveness. The examination of Level 2 screeners focuses on five instruments associated with published evaluations in peer-reviewed journals. Key criteria encompass the traditional indices of test integrity such as test reliability (inter-rater, test-retest) and construct validity, including concurrent and predictive validity, sensitivity (SE), and specificity (SP). These evaluations reveal limitations, including inadequate sample sizes, reliability issues, and limited involvement of independent researchers. Also lacking are comparative test evaluations under standardized conditions, hindering interpretation of differences in discriminative performance across instruments. Practical considerations constraining the use of such instruments—such as the requirements for training in test administration and test administration time—are canvassed. Published Level 2 screener short forms are reviewed and, as a consequence of that evaluation, future directions for assessing the discriminative capacity of items and measures are suggested. Suggested priorities for future research include targeting large and diverse samples to permit robust appraisals of Level 2 items and scales across the 12–36 month age range, a greater focus on precise operationalization of items and response coding to enhance reliability, ongoing exploration of potentially discriminating items at the younger end of the targeted age range, and trying to unravel the complexities of developmental trajectories in autistic infants. Finally, we emphasize the importance of understanding how screening efficacy is dependent on clinicians' and researchers' ability not only to develop screening tests but also to negotiate the complex organizational systems within which screening procedures must be implemented.

Early identification of autism, followed by appropriate intervention, has the potential to improve outcomes for autistic individuals. Numerous screening instruments have been developed for children under 3 years of age. Level 1 screeners are used in large-scale screening to detect at-risk children in the general population; Level 2 screeners are concerned with distinguishing children with signs of autism from those with other developmental problems. The focus here is evaluation of Level 2 screeners. However, given the contributions of Level 1 screeners and the necessity to understand how they might interface with Level 2 screeners, we briefly review Level 1 screeners and consider instrument characteristics and system variables that may constrain their effectiveness. The examination of Level 2 screeners focuses on five instruments associated with published evaluations in peer-reviewed journals. Key criteria encompass the traditional indices of test integrity such as test reliability (inter-rater, test-retest) and construct validity, including concurrent and predictive validity, sensitivity (SE), and specificity (SP). These evaluations reveal limitations, including inadequate sample sizes, reliability issues, and limited involvement of independent researchers. Also lacking are comparative test evaluations under standardized conditions, hindering interpretation of differences in discriminative performance across instruments. Practical considerations constraining the use of such instruments—such as the requirements for training in test administration and test administration time—are canvassed. Published Level 2 screener short forms are reviewed and, as a consequence of that evaluation, future directions for assessing the discriminative capacity of items and measures are suggested. Suggested priorities for future research include targeting large and diverse samples to permit robust appraisals of Level 2 items and scales across the 12–36 month age range, a greater focus on precise operationalization of items and response coding to enhance reliability, ongoing exploration of potentially discriminating items at the younger end of the targeted age range, and trying to unravel the complexities of developmental trajectories in autistic infants. Finally, we emphasize the importance of understanding how screening efficacy is dependent on clinicians' and researchers' ability not only to develop screening tests but also to negotiate the complex organizational systems within which screening procedures must be implemented. [ABSTRACT FROM AUTHOR]

Early identification of children with autism is necessary to support their social and communicative skills and cognitive, verbal, and adaptive development. Researchers have identified several barriers to early diagnosis. Data collected in low- and middle-income contexts—where the vast majority of children in the world live—is scarce. In Ecuador, as in many other countries, estimates are lower compared to the global prevalence. Health authorities estimate a prevalence of 0.28% (0.18%–0.41%) in children aged five years old or less. Based on the hypothesis that, as in many parts of the world, children in Ecuador are not routinely screened and that this situation may result from poor recognition of this condition, our objective was to identify potential obstacles to case identification in pediatric settings. Several barriers, consistent with those identified in other countries, were reported by 153 participants on a survey, including lack of time, lack of resources to refer, fear of unnecessarily alarming families, and lack of information to guide families with concerns. The vast majority of participants are aware of the need for a screening tool for autism detection but report a lack of knowledge of its formal application. Most of the barriers reported in this study could be overcome by educational programs tailored to professional needs in order to support the well-being of children with autism and their families.

Abstract Background Autism spectrum disorder (ASD) diagnosis can be delayed due in part to the time required for administration of standard exams, such as the Autism Diagnostic Observation Schedule (ADOS). Shorter and potentially mobilized approaches would help to alleviate bottlenecks in the healthcare system. Previous work using machine learning suggested that a subset of the behaviors measured by ADOS can achieve clinically acceptable levels of accuracy. Here we expand on this initial work to build sparse models that have higher potential to generalize to the clinical population. Methods We assembled a collection of score sheets for two ADOS modules, one for children with phrased speech (Module 2; 1319 ASD cases, 70 controls) and the other for children with verbal fluency (Module 3; 2870 ASD cases, 273 controls). We used sparsity/parsimony enforcing regularization techniques in a nested cross validation grid search to select features for 17 unique supervised learning models, encoding missing values as additional indicator features. We augmented our feature sets with gender and age to train minimal and interpretable classifiers capable of robust detection of ASD from non-ASD. Results By applying 17 unique supervised learning methods across 5 classification families tuned for sparse use of features and to be within 1 standard error of the optimal model, we find reduced sets of 10 and 5 features used in a majority of models. We tested the performance of the most interpretable of these sparse models, including Logistic Regression with L2 regularization or Linear SVM with L1 regularization. We obtained an area under the ROC curve of 0.95 for ADOS Module 3 and 0.93 for ADOS Module 2 with less than or equal to 10 features. Conclusions The resulting models provide improved stability over previous machine learning efforts to minimize the time complexity of autism detection due to regularization and a small parameter space. These robustness techniques yield classifiers that are sparse, interpretable and that have potential to generalize to alternative modes of autism screening, diagnosis and monitoring, possibly including analysis of short home videos.

There are concerns that females with autism spectrum disorders (ASD) may be underidentified because of factors such as better camouflaging and poorer recognition of the signs of ASD in females. One stage at which females may be underidentified is during screening. In this study, we, therefore, evaluated whether the autism spectrum quotient (AQ-10), a brief recommended screening instrument for ASD in adults suspected of having ASD, showed any evidence of underestimating symptoms in females. Our results broadly replicate those of an earlier study in finding no strong evidence that the AQ-10 is biased against females. However, to achieve better performance in females, we suggest that one item be replaced with an item measuring more "female" manifestations of ASD. [ABSTRACT FROM AUTHOR]

ASD (autism spectrum disorder) is a complex developmental and neurological disorder that impacts the social life of the affected person by disturbing their capability for interaction and communication. As it is a behavioural disorder, early treatment will improve the quality of life of ASD patients. Traditional screening is carried out with behavioural assessment through trained physicians, which is expensive and time-consuming. To resolve the issue, several conventional methods strive to achieve an effective ASD identification system, but are limited by handling large data sets, accuracy, and speed. Therefore, the proposed identification system employed the MBA (modified bat) algorithm based on ANN (artificial neural networks), modified ANN (modified artificial neural networks), DT (decision tree), and KNN (k-nearest neighbours) for the classification of ASD in children and adolescents. A BA (bat algorithm) is utilised for the automatic zooming capability, which improves the system's efficacy by excellently finding the solutions in the identification system. Conversely, BA is effective in the identification, it still has certain drawbacks like speed, accuracy, and falls into local extremum. Therefore, the proposed identification system modifies the BA optimisation with random perturbation of trends and optimal orientation. The dataset utilised in the respective model is the Q-chat-10 dataset. This dataset contains data of four stages of age groups such as toddlers, children, adolescents, and adults. To analyse the quality of the dataset, dataset evaluation mechanism, such as the Chi-Squared Statistic and p -value, are used in the respective research. The evaluation signifies the relation of the dataset with respect to the proposed model. Further, the performance of the proposed detection system is examined with certain performance metrics to calculate its efficiency. The outcome revealed that the modified ANN classifier model attained an accuracy of 1.00, ensuring improved performance when compared with other state-of-the-art methods. Thus, the proposed model was intended to assist physicians and researchers in enhancing the diagnosis of ASD to improve the standard of life of ASD patients.

Purpose: To compare the utility of single versus repeated autism screening in a sample at higher likelihood (HL) for ASD, following both screen positives and all screen negatives to diagnostic outcome., Methods: Using a prospective infant sibling design, the current study followed 135 toddlers at HL for ASD and conducted diagnostic evaluations on the full sample at 18, 24, and 36 months. The psychometric properties of the M-CHAT-R using both concurrent and predictive diagnostic evaluations were compared in a group screened once (at 18 months only, n = 60) or twice (at both 18 and 24 months, n = 75). The study also examined consistency in reporting of ASD symptoms across the M-CHAT-R and a developmental concerns interview, comparing the HL group to a group with lower likelihood (LL) for ASD (n = 88)., Results: Sensitivity and specificity of the M-CHAT-R were high (75 - 95%), consistent with previous research. Positive predictive value (43 - 76%) was higher in this HL group than in previous community samples. Repeat screening improved sensitivity with little cost to specificity. At both 18 and 24 months, HL parents were more consistent in their reporting on the M-CHAT-R and a concerns interview than LL parents., Conclusion: The M-CHAT-R has strong psychometric properties when used with groups at HL for ASD, suggesting that scores over the screening cutoff of 3 should lead to prompt diagnostic evaluation referrals in children with older siblings on the spectrum., (© 2023. The Author(s).)

Background & objectives: There is a paucity of trained professionals for the diagnosis of autism spectrum disorder (ASD), and a large number of cases go undetected and are diagnosed only during adolescence. There is no screening instrument specifically developed for screening of Indian population for ASD. This study was undertaken to develop a screening instrument to screen ASD in north Indian Hindi speaking population by multipurpose health workers. Methods: A 37-item instrument in Hindi with dichotomous yes/no responses [Chandigarh Autism Screening Instrument (CASI)] was developed to be applied on children aged 1.5-10 yr. The instrument was pilot tested and then reliability and validity of this instrument were tested. The sample included children with intellectual disability (n=75), ASD (n=83), other developmental disorders (n=87) and typically developing children (n=160). Results: Reliability, construct and content validity testing of the instrument were performed, and a score of 10 as cut-off had sensitivity of 89.16 per cent, specificity of 89.13 per cent, positive predictive value of 67.89 per cent and negative predictive value of 96.96 per cent. A shorter four-item version (CASI Bref) has also been developed with good sensitivity (73.49%) and specificity (90.68%) at a cut-off score of 2. Interpretation & conclusions: CASI was found to be a valid instrument for screening general Hindi speaking population of north India with adequate sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Background: Autism spectrum disorder (ASD) diagnosis can be delayed due in part to the time required for administration of standard exams, such as the Autism Diagnostic Observation Schedule (ADOS). Shorter and potentially mobilized approaches would help to alleviate bottlenecks in the healthcare system. Previous work using machine learning suggested that a subset of the behaviors measured by ADOS can achieve clinically acceptable levels of accuracy. Here we expand on this initial work to build sparse models that have higher potential to generalize to the clinical population. Methods: We assembled a collection of score sheets for two ADOS modules, one for children with phrased speech (Module 2; 1319 ASD cases, 70 controls) and the other for children with verbal fluency (Module 3; 2870 ASD cases, 273 controls). We used sparsity/parsimony enforcing regularization techniques in a nested cross validation grid search to select features for 17 unique supervised learning models, encoding missing values as additional indicator features. We augmented our feature sets with gender and age to train minimal and interpretable classifiers capable of robust detection of ASD from non-ASD. Results: By applying 17 unique supervised learning methods across 5 classification families tuned for sparse use of features and to be within 1 standard error of the optimal model, we find reduced sets of 10 and 5 features used in a majority of models. We tested the performance of the most interpretable of these sparse models, including Logistic Regression with L2 regularization or Linear SVM with L1 regularization. We obtained an area under the ROC curve of 0.95 for ADOS Module 3 and 0.93 for ADOS Module 2 with less than or equal to 10 features. Conclusions: The resulting models provide improved stability over previous machine learning efforts to minimize the time complexity of autism detection due to regularization and a small parameter space. These robustness techniques yield classifiers that are sparse, interpretable and that have potential to generalize to alternative modes of autism screening, diagnosis and monitoring, possibly including analysis of short home videos. [ABSTRACT FROM AUTHOR]

BackgroundBoth parent-report and clinician-administered autism spectrum disorder (ASD) screening instruments are important to accurately inform ASD risk ascertainment. The aim of this study was to adapt a clinician-administered ASD screening instrument, the Autistic Behavioural Indicators Instrument (ABII), as a parent questionnaire equivalent (ABII-PQ). Method The modification of ABII items into parent questions is described. The ABII-PQ was trialled in a sample of parents of children, aged between 12 months and 6 years, with an ASD (n = 65,Mchild age = 4.03 years) or typical development (n = 37,Mchild age = 2.09 years). ResultsInternal consistency was high,α = .92. Receiver operator curves analysis identified the optimal ABII-PQ cut-off score, which yielded high sensitivity (.97) and specificity (.95). Classification accuracy was high for children across the autism spectrum (autistic disorder:n = 35, 100%; Asperger syndrome:n = 14, 93%; pervasive developmental disorder not otherwise specified:n = 14, 93%). ConclusionThe ABII-PQ shows promise as a parent questionnaire version of the ABII. [ABSTRACT FROM PUBLISHER]

This study examined the effect of age at completion of an autism screening test on item failure rates contrasting older (>20 months) with younger (<20 months) toddlers in a community primary care sample of 73,564 children. Items related to social development were categorized into one of three age sets per criteria from Inada et al. (Research in Autism Spectrum Disorders 4(4):605-611, 2010). Younger toddlers produced higher rates of item failure than older toddlers and items in both of the later acquired item sets had higher probability rates for failure than the earliest acquired item set (prior to 8 months). Use of the same items and the same scoring throughout the target age range for autism screening may not be the best strategy for identifying the youngest toddlers at risk for autism. [ABSTRACT FROM AUTHOR]

Accuracy of autism screening using M-CHAT plus the follow-up interview (M-CHAT/F) for children screened positive at 18-months was compared to screening at 24-months. Formal ASD testing was criterion for a community sample of M-CHAT positive children (n = 98), positive predictive value (PPV) was 0.40 for the M-CHAT and 0.58 for the M-CHAT/F. MCHAT/F PPV was 0.69 among children 20+ months compared to 0.36 for <20 months. Multivariate analyses incorporating data from the Ages and Stages Questionnaire, MacArthur-Bates Communicative Development Inventory, M-CHAT and M-CHAT/F results, and M-CHAT items suggest language variables carry greatest relative importance in contributing to an age-based algorithm with potential to improve PPV for toddlers <20 months to the same level as observed in older toddlers. [ABSTRACT FROM AUTHOR]

The Mobile Autism Risk Assessment (MARA) is a new, electronically administered, 7-question autism spectrum disorder (ASD) screen to triage those at highest risk for ASD. Children 16 months-17 years (N = 222) were screened during their first visit in a developmental-behavioral pediatric clinic. MARA scores were compared to diagnosis from the clinical encounter. Participant median age was 5.8 years, 76.1 % were male, and most participants had an intelligence/developmental quotient score >85; 69 of the participants (31 %) received a clinical diagnosis of ASD. The sensitivity of the MARA in detecting ASD was 89.9 % [95 % CI = 82.7-97]; the specificity was 79.7 % [95 % CI = 73.4-86.1]. In a high-risk clinical setting, the MARA shows promise as a screen to distinguish ASD from other developmental/behavioral disorders. [ABSTRACT FROM AUTHOR]

Increased public awareness of autism spectrum disorders (ASD) and routine screening in primary care have contributed to increased requests for diagnostic ASD evaluations. However, given the scarcity of subspecialty autism diagnostic resources, overreferral of children suspected of having ASD may be contributing to long waiting lists at tertiary care autism centers and delaying diagnosis for those children who truly have ASD. To determine whether children are being excessively referred to ASD-specific diagnostic clinics, our objective was to determine the prevalence of true ASD diagnoses in children referred for diagnostic ASD evaluation. Charts of all patients referred to a regional autism center between April 2011 and August 2012 for suspicion of a possible ASD were retrospectively reviewed and demographic and clinical diagnoses abstracted. Only 214 of 348 patients evaluated (61%) received an ASD diagnosis. Thus, concerns about autism are not confirmed by an ASD diagnosis in a significant number of children. [ABSTRACT FROM AUTHOR]

The Autism Detection in Early Childhood (ADEC) is a brief, play-based screening tool for the assessment of autism spectrum disorder (ASD) in children aged 12-36 months. We examined the psychometric properties of the ADEC in a clinical sample of toddlers ( n = 114) referred to a US pediatric hospital for assessment due to concerns of developmental delay or ASD. The ADEC (cutoff = 11) returned good sensitivity (.93-.94) but poorer specificity (.62-.64) for best estimate clinical diagnosis of ASD, and compared favorably with the ADOS-2. Internal consistency was acceptable, α = .80, and inter-rater reliability was high, ICC = .95. Results support the use of the ADEC as a clinical screen for ASD. [ABSTRACT FROM AUTHOR]

Recent research shows that Human Robot Interaction (HRI) can play an important role in the screening and therapy of many disorders, and suggests broad range of applications for children with developmental disorders. This paper presents the design, development, and application of a parrot-like robot as a screening tool to diagnose autistic children. The interactions of several autistic children with the robot have been observed and compared to the interaction of a group of normal children with the robot to determine the important behavioral features separating the autistic children from the normal ones. Based on these observations, we developed a descriptive research methodology to specify important and discriminating features for screening autism using behavioral aspects in social interaction, communication, and stereotyped and repetitive behaviors. Our experiments show that there are significant differences between autistic and normal children pertaining to their communication with the robot, repetitive behaviors and sustaining attention. The results show the effectiveness of using robots in screening developmental disorders such as autism, highlighting the importance of Robot Assisted Screening (RAS) or intelligent toys in this field. We have determined 6 features extracted from the children's behavior, and Random Forest method is used to distinguish between the autistic and normal children. The system has been tested on 51 children, 35 autistic and 16 normal, with maximum 90 % correct classification rate. [ABSTRACT FROM AUTHOR]

AimWe explored potential barriers to adoption of recommended screening for autism by family physicians at 18- and 24-month well-child visits.BackgroundThe American Academy of Pediatrics recommends early detection and intervention of autism through the use of a standardized autism-specific screening tool on all children at the 18- and 24-month well-child visits. However, not all family physicians screen for autism.MethodsThree focus groups and six semi-structured interviews were conducted with 15 family physicians in the Kansas City metropolitan area. Verbatim transcripts were inductively coded; data were analyzed using standard text analysis.FindingsParticipants had differing views on the increased incidence of autism. Most participants attributed the increase to changes in diagnostic criteria. There was no consensus on the benefit of implementing universal screening for autism during the 18- or 24-month visit. Many preferred to identify potential problems through general developmental assessments and observations. No participants used specific screening tools for autism, and only one participant was aware of such a tool (M-CHAT). Lack of adequate training on child development and screening methods as well as limited availability of community-based resources to manage children with autism was seen as major barriers to routine screening. Suggested solutions included working toward a stronger evidence base, improving physician training and continuing education, and making systemic changes in healthcare. In conclusion, universal screening for autism at the 18- and 24-month visits is not widely accepted, nor is it implemented by family physicians. [ABSTRACT FROM PUBLISHER]

In this study, a dynamic screening strategy is proposed to discriminate subjects with autistic spectrum disorder (ASD) from healthy controls. The ASD is defined as a neurodevelopmental disorder that disrupts normal patterns of connectivity between the brain regions. Therefore, the potential use of such abnormality for autism screening is investigated. The connectivity patterns are estimated from electroencephalogram (EEG) data collected from 8 brain regions under various mental states. The EEG data of 12 healthy controls and 6 autistic children (age matched in 7–10) were collected during eyes-open and eyes-close resting states as well as when subjects were exposed to affective faces (happy, sad and calm). Subsequently, the subjects were classified as autistic or healthy groups based on their brain connectivity patterns using pattern recognition techniques. Performance of the proposed system in each mental state is separately evaluated. The results present higher recognition rates using functional connectivity features when compared against other existing feature extraction methods. [ABSTRACT FROM AUTHOR]